• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

A Many-objective Particle Swarm Optimization Algorithm Based on Multiple Criteria for Hybrid Recommendation System


Abstract

Nowadays, recommendation systems (RSs) are applied to all aspects of online life. In order to overcome the problem that individuals who do not meet the constraints need to be regenerated when the many-objective evolutionary algorithm (MaOEA) solves the hybrid recommendation model, this paper proposes a many-objective particle swarm optimization algorithm based on multiple criteria (MaPSO-MC). A generation-based fitness evaluation strategy with diversity enhancement (GBFE-DE) and ISDE+ are coupled to comprehensively evaluate individual performance. At the same time, according to the characteristics of the model, the regional optimization has an impact on the individual update, and a many-objective evolutionary strategy based on bacterial foraging (MaBF) is used to improve the algorithm search speed. Experimental results prove that this algorithm has excellent convergence and diversity, and can produce accurate, diverse, novel and high coverage recommendations when solving recommendation models.


Statistics

Show / Hide Statistics

Statistics (Cumulative Counts from December 1st, 2015)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article

[IEEE Style]
Z. Hu, Y. Lan, Z. Zhang and X. Cai, "A Many-objective Particle Swarm Optimization Algorithm Based on Multiple Criteria for Hybrid Recommendation System," KSII Transactions on Internet and Information Systems, vol. 15, no. 2, pp. 442-460, 2021. DOI: 10.3837/tiis.2021.02.004.

[ACM Style]
Zhaomin Hu, Yang Lan, Zhixia Zhang, and Xingjuan Cai. 2021. A Many-objective Particle Swarm Optimization Algorithm Based on Multiple Criteria for Hybrid Recommendation System. KSII Transactions on Internet and Information Systems, 15, 2, (2021), 442-460. DOI: 10.3837/tiis.2021.02.004.